Origins of Machine Learning

The origins of Machine Learning go back to the year 1959. The term "machine learning" was coined in this year by Arthur Lee Samuel. He wrote a Checkers-playing Program which is considered to be the first self-learning program.

The Real Problem

"People worry that computers will get too smart and take over the world, but the real problem is that they're too stupid and they've already taken over the world." (Pedro Domingos)

Pedro Domingos is Professor at University of Washington. He is a researcher in machine learning and known for markov logic network enabling uncertain inference.

Learning

"Tell me and I forget, teach me and I may remember, involve me and I learn."
(Benjamin Franklin)

"The more I read, the more I acquire, the more certain I am that I know nothing."
(Voltaire)

"You live and learn. At any rate, you live."
(Douglas Adams, Mostly Harmless)

If learning means living, some computers live!

"In learning you will teach, and in teaching you will learn."
(Phil Collins)
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Quote of the Day:

"If programmers deserve to be rewarded for creating innovative programs, by the same token
they deserve to be punished if they restrict the use of these programs. " (Richard Stallmann)

If you have the choice working with Python 2 or Python 3, we recomend to switch to Python 3!
You can read our Python Tutorial to see what the differences are.

Tutorial and Online Course

Machine Learning

This is a completely new and incomplete chapter of our tutorial! We started work in January 2017!

Machine learning is is the kind of programming which gives computers the capability to automatically learn from data
without being explicitly programmed. This means in other words that these programs change their behaviour by learning
from data.

Machine learning can be roughly separated into three categories:

Supervised learning

The machine learning program is both given the input data and the corresponding labelling. This means that the learn data has to be labelled by a human being beforehand.

Unsupervised learning

No labels are provided to the learning algorithm. The algorithm has to figure out the a clustering of the input data.

Reinforcement learning

A computer program dynamically interacts with its environment. This means that the program receives positive and/or negative feedback to improve it performance.